Real-time flood forecasting using time-varying parameter hydrological model: case study for Ta Trach reservoir

• Published in: Applied water science Vol. 15; no. 7; pp. 152 - 13
• Main Authors: Tran, Chau Kim, Dang, Nguyen Dong, Nguyen, Dang Mai, Nguyen, Bac Thi Ngoc, Le, Binh Thi Hoa, Vo, Hoang Cong, La, Hien Phu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2025; Springer Nature B.V; SpringerOpen
• Subjects: Aquatic Pollution; Artificial intelligence; Comparative Law; Earth and Environmental Science; Earth Sciences; Flood flow; Flood forecasting; Flood management; Floods; Forecasting; Historic floods; Hydrogeology; Hydrologic models; Hydrological model; Hydrology; Industrial and Production Engineering; Inflow; Inflow discharge; International & Foreign Law; Nanotechnology; Original Article; Parameters; Private International Law; Rainfall; Real time; Real-time flood forecasting; Reservoir management; Reservoir operation; Ta Trach reservoir; Time-varying parameters; Waste Water Technology; Water Industry/Water Technologies; Water Management; Water Pollution Control; Ta Trach reservoir; Time-varying parameters; Inflow discharge; Hydrological model; Real-time flood forecasting
• ISSN: 2190-5487; 2190-5495
• DOI: 10.1007/s13201-025-02503-4

Flood forecasting for reservoir operation is a complex and challenging subject. It is, however, fundamental for minimizing damage and maximizing economic efficiency in reservoir management. Currently, real-time flood forecasting represents an essential trend on a global scale. This study introduces a real-time flood forecasting approach using a time-varying parameter hydrological model, applied to forecast inflows to Ta Trach reservoir in the historical flood season in 2020. The model dynamically updates parameters to reflect basin conditions in every time steps. Notably, the study’s method achieves high accuracy with Nash–Sutcliffe Efficiency values of 99.32, 95.7, and 89.14% for 1-h, 3-h, and 6-h lead times, respectively. Results surpass traditional fixed parameter and artificial intelligence models. Moreover, requiring only updated rainfall and inflow data, the model is computationally efficient, compatible with existing infrastructure in research area. With these advantages, the method presented in the study has opened a new approach and is suitable for broader applications in flood flow forecasting.